Overview

Dataset statistics

Number of variables23
Number of observations159
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.7 KiB
Average record size in memory184.8 B

Variable types

Categorical6
Numeric16
Boolean1

Alerts

How many social media platforms are you on? has a high cardinality: 63 distinct valuesHigh cardinality
Enter Marks(percentage) For 10th Grade: is highly overall correlated with Department:High correlation
How often do you use social media ? (In hours) is highly overall correlated with Impacted By GadgetHigh correlation
Time spend on Facebook? (In hours) is highly overall correlated with How many social media platforms are you on?High correlation
Time spend on Discord? (In hours) is highly overall correlated with How many social media platforms are you on? and 1 other fieldsHigh correlation
Time spend on Pinterest? (In hours) is highly overall correlated with Time spend on Reddit? (In hours)High correlation
Time spend on Twitter? (In hours) is highly overall correlated with How many social media platforms are you on? and 1 other fieldsHigh correlation
Time spend on Telegram? (In hours) is highly overall correlated with Time spend on Reddit? (In hours)High correlation
Overall time spent on other application? (In hours) is highly overall correlated with Time spend on Reddit? (In hours)High correlation
Department: is highly overall correlated with Enter Marks(percentage) For 10th Grade: and 1 other fieldsHigh correlation
Choose Current year: is highly overall correlated with Department:High correlation
How many social media platforms are you on? is highly overall correlated with Time spend on Facebook? (In hours) and 3 other fieldsHigh correlation
Time spend on Reddit? (In hours) is highly overall correlated with Time spend on Discord? (In hours) and 5 other fieldsHigh correlation
Impacted By Gadget is highly overall correlated with How often do you use social media ? (In hours)High correlation
Time spend on Reddit? (In hours) is highly imbalanced (87.8%)Imbalance
Time spent on WhatsApp? (In hours) has 2 (1.3%) zerosZeros
Time spend on Instagram? (In hours) has 36 (22.6%) zerosZeros
Time spend on Facebook? (In hours) has 146 (91.8%) zerosZeros
Time spend on YouTube? (In hours) has 12 (7.5%) zerosZeros
Time spend on Snapchat? (In hours) has 61 (38.4%) zerosZeros
Time spend on Discord? (In hours) has 141 (88.7%) zerosZeros
Time spend on Pinterest? (In hours) has 133 (83.6%) zerosZeros
Time spend on Twitter? (In hours) has 136 (85.5%) zerosZeros
Time spend on Telegram? (In hours) has 131 (82.4%) zerosZeros
Overall time spent on other application? (In hours) has 34 (21.4%) zerosZeros
What is the maximum time that you have spent away from your phone? (In hours) has 3 (1.9%) zerosZeros
Entertainment usage time while using phone(per day) (In hours) has 4 (2.5%) zerosZeros
Productivity and finance time while using phone (per day)(In hours) has 22 (13.8%) zerosZeros

Reproduction

Analysis started2023-04-10 09:56:08.466551
Analysis finished2023-04-10 10:00:22.953208
Duration4 minutes and 14.49 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Department:
Categorical

Distinct6
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Computer
91 
Artificial Intelligence
31 
Electronics
23 
Information Technology
 
7
Electrical
 
4

Length

Max length23
Median length8
Mean length12.062893
Min length8

Characters and Unicode

Total characters1918
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArtificial Intelligence
2nd rowArtificial Intelligence
3rd rowArtificial Intelligence
4th rowArtificial Intelligence
5th rowArtificial Intelligence

Common Values

ValueCountFrequency (%)
Computer 91
57.2%
Artificial Intelligence 31
 
19.5%
Electronics 23
 
14.5%
Information Technology 7
 
4.4%
Electrical 4
 
2.5%
Mechanical 3
 
1.9%

Length

2023-04-10T15:30:23.288380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T15:30:23.459596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
computer 91
46.2%
artificial 31
 
15.7%
intelligence 31
 
15.7%
electronics 23
 
11.7%
information 7
 
3.6%
technology 7
 
3.6%
electrical 4
 
2.0%
mechanical 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 221
11.5%
t 187
 
9.7%
i 161
 
8.4%
r 156
 
8.1%
o 142
 
7.4%
l 134
 
7.0%
c 129
 
6.7%
n 109
 
5.7%
m 98
 
5.1%
C 91
 
4.7%
Other values (14) 490
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1683
87.7%
Uppercase Letter 197
 
10.3%
Space Separator 38
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 221
13.1%
t 187
11.1%
i 161
9.6%
r 156
9.3%
o 142
8.4%
l 134
8.0%
c 129
7.7%
n 109
6.5%
m 98
 
5.8%
p 91
 
5.4%
Other values (7) 255
15.2%
Uppercase Letter
ValueCountFrequency (%)
C 91
46.2%
I 38
19.3%
A 31
 
15.7%
E 27
 
13.7%
T 7
 
3.6%
M 3
 
1.5%
Space Separator
ValueCountFrequency (%)
38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1880
98.0%
Common 38
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 221
11.8%
t 187
9.9%
i 161
 
8.6%
r 156
 
8.3%
o 142
 
7.6%
l 134
 
7.1%
c 129
 
6.9%
n 109
 
5.8%
m 98
 
5.2%
C 91
 
4.8%
Other values (13) 452
24.0%
Common
ValueCountFrequency (%)
38
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 221
11.5%
t 187
 
9.7%
i 161
 
8.4%
r 156
 
8.1%
o 142
 
7.4%
l 134
 
7.0%
c 129
 
6.7%
n 109
 
5.7%
m 98
 
5.1%
C 91
 
4.7%
Other values (14) 490
25.5%
Distinct3
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
First year
63 
Second year
49 
Third year
47 

Length

Max length11
Median length10
Mean length10.308176
Min length10

Characters and Unicode

Total characters1639
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThird year
2nd rowFirst year
3rd rowFirst year
4th rowFirst year
5th rowFirst year

Common Values

ValueCountFrequency (%)
First year 63
39.6%
Second year 49
30.8%
Third year 47
29.6%

Length

2023-04-10T15:30:23.606179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T15:30:23.735684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
year 159
50.0%
first 63
 
19.8%
second 49
 
15.4%
third 47
 
14.8%

Most occurring characters

ValueCountFrequency (%)
r 269
16.4%
e 208
12.7%
159
9.7%
y 159
9.7%
a 159
9.7%
i 110
 
6.7%
d 96
 
5.9%
F 63
 
3.8%
s 63
 
3.8%
t 63
 
3.8%
Other values (6) 290
17.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1321
80.6%
Space Separator 159
 
9.7%
Uppercase Letter 159
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 269
20.4%
e 208
15.7%
y 159
12.0%
a 159
12.0%
i 110
8.3%
d 96
 
7.3%
s 63
 
4.8%
t 63
 
4.8%
c 49
 
3.7%
o 49
 
3.7%
Other values (2) 96
 
7.3%
Uppercase Letter
ValueCountFrequency (%)
F 63
39.6%
S 49
30.8%
T 47
29.6%
Space Separator
ValueCountFrequency (%)
159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1480
90.3%
Common 159
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 269
18.2%
e 208
14.1%
y 159
10.7%
a 159
10.7%
i 110
7.4%
d 96
 
6.5%
F 63
 
4.3%
s 63
 
4.3%
t 63
 
4.3%
S 49
 
3.3%
Other values (5) 241
16.3%
Common
ValueCountFrequency (%)
159
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 269
16.4%
e 208
12.7%
159
9.7%
y 159
9.7%
a 159
9.7%
i 110
 
6.7%
d 96
 
5.9%
F 63
 
3.8%
s 63
 
3.8%
t 63
 
3.8%
Other values (6) 290
17.7%
Distinct93
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.849371
Minimum49
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:23.986742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile61.8
Q178.5
median86
Q389.4
95-th percentile94.546
Maximum98
Range49
Interquartile range (IQR)10.9

Descriptive statistics

Standard deviation9.7605626
Coefficient of variation (CV)0.11781094
Kurtosis1.1926981
Mean82.849371
Median Absolute Deviation (MAD)5.2
Skewness-1.1861339
Sum13173.05
Variance95.268582
MonotonicityNot monotonic
2023-04-10T15:30:24.149504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 7
 
4.4%
80 5
 
3.1%
85 5
 
3.1%
90.6 5
 
3.1%
89 5
 
3.1%
80.2 4
 
2.5%
87 4
 
2.5%
92 3
 
1.9%
89.2 3
 
1.9%
82.2 3
 
1.9%
Other values (83) 115
72.3%
ValueCountFrequency (%)
49 1
0.6%
52 1
0.6%
56 1
0.6%
57 2
1.3%
59.78 1
0.6%
60 2
1.3%
62 1
0.6%
63.8 1
0.6%
64.6 1
0.6%
65 2
1.3%
ValueCountFrequency (%)
98 1
0.6%
96.6 1
0.6%
95.5 1
0.6%
95.2 2
1.3%
95 2
1.3%
94.6 1
0.6%
94.54 1
0.6%
94 2
1.3%
93.8 2
1.3%
93.4 1
0.6%
Distinct102
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.554591
Minimum52
Maximum92.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:24.292942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile60
Q172.865
median79
Q383.9
95-th percentile89.743
Maximum92.84
Range40.84
Interquartile range (IQR)11.035

Descriptive statistics

Standard deviation8.8191917
Coefficient of variation (CV)0.11371592
Kurtosis0.19064826
Mean77.554591
Median Absolute Deviation (MAD)5.2
Skewness-0.73680255
Sum12331.18
Variance77.778141
MonotonicityNot monotonic
2023-04-10T15:30:24.433899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 8
 
5.0%
75 6
 
3.8%
60 5
 
3.1%
86 5
 
3.1%
82 5
 
3.1%
78 5
 
3.1%
79 4
 
2.5%
73 3
 
1.9%
91.89 3
 
1.9%
89 3
 
1.9%
Other values (92) 112
70.4%
ValueCountFrequency (%)
52 1
 
0.6%
53 1
 
0.6%
54.87 1
 
0.6%
56.9 1
 
0.6%
57 1
 
0.6%
60 5
3.1%
61.2 1
 
0.6%
61.57 1
 
0.6%
61.6 1
 
0.6%
62 1
 
0.6%
ValueCountFrequency (%)
92.84 1
 
0.6%
91.89 3
1.9%
91.78 1
 
0.6%
90 2
1.3%
89.86 1
 
0.6%
89.73 1
 
0.6%
89.44 1
 
0.6%
89 3
1.9%
88.89 1
 
0.6%
88.87 1
 
0.6%
Distinct7
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3962264
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:24.553067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q16
median6
Q37
95-th percentile9
Maximum10
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4098158
Coefficient of variation (CV)0.22041368
Kurtosis0.2121713
Mean6.3962264
Median Absolute Deviation (MAD)1
Skewness0.3381302
Sum1017
Variance1.9875806
MonotonicityNot monotonic
2023-04-10T15:30:24.637841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 50
31.4%
7 43
27.0%
5 20
 
12.6%
8 19
 
11.9%
4 17
 
10.7%
10 6
 
3.8%
9 4
 
2.5%
ValueCountFrequency (%)
4 17
 
10.7%
5 20
 
12.6%
6 50
31.4%
7 43
27.0%
8 19
 
11.9%
9 4
 
2.5%
10 6
 
3.8%
ValueCountFrequency (%)
10 6
 
3.8%
9 4
 
2.5%
8 19
 
11.9%
7 43
27.0%
6 50
31.4%
5 20
 
12.6%
4 17
 
10.7%
Distinct12
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Educational, Entertainment, Research/Reading, Gaming
34 
Educational, Entertainment, Research/Reading
27 
Entertainment
26 
Educational, Entertainment
25 
Educational
17 
Other values (7)
30 

Length

Max length52
Median length39
Mean length30.981132
Min length6

Characters and Unicode

Total characters4926
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowEntertainment
2nd rowEducational, Entertainment, Research/Reading, Gaming
3rd rowEducational, Entertainment
4th rowEducational, Entertainment, Research/Reading
5th rowEntertainment

Common Values

ValueCountFrequency (%)
Educational, Entertainment, Research/Reading, Gaming 34
21.4%
Educational, Entertainment, Research/Reading 27
17.0%
Entertainment 26
16.4%
Educational, Entertainment 25
15.7%
Educational 17
10.7%
Educational, Entertainment, Gaming 10
 
6.3%
Entertainment, Research/Reading 6
 
3.8%
Gaming 4
 
2.5%
Educational, Research/Reading 4
 
2.5%
Research/Reading 3
 
1.9%
Other values (2) 3
 
1.9%

Length

2023-04-10T15:30:24.750551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
entertainment 131
35.0%
educational 117
31.3%
research/reading 75
20.1%
gaming 51
 
13.6%

Most occurring characters

ValueCountFrequency (%)
n 636
12.9%
a 566
11.5%
t 510
 
10.4%
e 487
 
9.9%
i 374
 
7.6%
E 248
 
5.0%
215
 
4.4%
, 215
 
4.4%
r 206
 
4.2%
d 192
 
3.9%
Other values (11) 1277
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3972
80.6%
Uppercase Letter 449
 
9.1%
Other Punctuation 290
 
5.9%
Space Separator 215
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 636
16.0%
a 566
14.2%
t 510
12.8%
e 487
12.3%
i 374
9.4%
r 206
 
5.2%
d 192
 
4.8%
c 192
 
4.8%
m 182
 
4.6%
g 126
 
3.2%
Other values (5) 501
12.6%
Uppercase Letter
ValueCountFrequency (%)
E 248
55.2%
R 150
33.4%
G 51
 
11.4%
Other Punctuation
ValueCountFrequency (%)
, 215
74.1%
/ 75
 
25.9%
Space Separator
ValueCountFrequency (%)
215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4421
89.7%
Common 505
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 636
14.4%
a 566
12.8%
t 510
11.5%
e 487
11.0%
i 374
8.5%
E 248
 
5.6%
r 206
 
4.7%
d 192
 
4.3%
c 192
 
4.3%
m 182
 
4.1%
Other values (8) 828
18.7%
Common
ValueCountFrequency (%)
215
42.6%
, 215
42.6%
/ 75
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 636
12.9%
a 566
11.5%
t 510
 
10.4%
e 487
 
9.9%
i 374
 
7.6%
E 248
 
5.0%
215
 
4.4%
, 215
 
4.4%
r 206
 
4.2%
d 192
 
3.9%
Other values (11) 1277
25.9%

How many social media platforms are you on?
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct63
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
WhatsApp, Instagram, YouTube, Snapchat
28 
WhatsApp, YouTube
14 
WhatsApp, Instagram, YouTube
13 
WhatsApp, Instagram, YouTube, Snapchat, Pinterest
WhatsApp, YouTube, Snapchat
 
8
Other values (58)
87 

Length

Max length95
Median length85
Mean length43.427673
Min length7

Characters and Unicode

Total characters6905
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)26.4%

Sample

1st rowWhatsApp
2nd rowWhatsApp, Instagram, YouTube, Snapchat
3rd rowWhatsApp, YouTube
4th rowWhatsApp, Instagram, YouTube, Snapchat, Pinterest, Twitter, Telegram
5th rowWhatsApp, Instagram, Facebook, YouTube, Snapchat, Pinterest, Telegram

Common Values

ValueCountFrequency (%)
WhatsApp, Instagram, YouTube, Snapchat 28
17.6%
WhatsApp, YouTube 14
 
8.8%
WhatsApp, Instagram, YouTube 13
 
8.2%
WhatsApp, Instagram, YouTube, Snapchat, Pinterest 9
 
5.7%
WhatsApp, YouTube, Snapchat 8
 
5.0%
WhatsApp, Instagram, Facebook, YouTube, Snapchat 5
 
3.1%
WhatsApp, Instagram, YouTube, Snapchat, Telegram 4
 
2.5%
WhatsApp, Instagram, YouTube, Snapchat, Pinterest, Twitter, Telegram 4
 
2.5%
WhatsApp, YouTube, Telegram 4
 
2.5%
WhatsApp, Instagram, YouTube, Snapchat, Twitter 4
 
2.5%
Other values (53) 66
41.5%

Length

2023-04-10T15:30:24.875676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
whatsapp 156
21.4%
youtube 149
20.4%
instagram 122
16.7%
snapchat 111
15.2%
telegram 48
 
6.6%
pinterest 37
 
5.1%
twitter 37
 
5.1%
facebook 28
 
3.8%
discord 27
 
3.7%
reddit 10
 
1.4%
Other values (4) 4
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a 699
 
10.1%
582
 
8.4%
, 580
 
8.4%
t 549
 
8.0%
p 423
 
6.1%
e 395
 
5.7%
s 343
 
5.0%
u 298
 
4.3%
r 272
 
3.9%
n 270
 
3.9%
Other values (23) 2494
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4710
68.2%
Uppercase Letter 1033
 
15.0%
Space Separator 582
 
8.4%
Other Punctuation 580
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 699
14.8%
t 549
11.7%
p 423
9.0%
e 395
8.4%
s 343
 
7.3%
u 298
 
6.3%
r 272
 
5.8%
n 270
 
5.7%
h 268
 
5.7%
o 234
 
5.0%
Other values (10) 959
20.4%
Uppercase Letter
ValueCountFrequency (%)
T 235
22.7%
W 156
15.1%
A 156
15.1%
Y 149
14.4%
I 122
11.8%
S 111
10.7%
P 38
 
3.7%
F 28
 
2.7%
D 27
 
2.6%
R 10
 
1.0%
Space Separator
ValueCountFrequency (%)
582
100.0%
Other Punctuation
ValueCountFrequency (%)
, 580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5743
83.2%
Common 1162
 
16.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 699
 
12.2%
t 549
 
9.6%
p 423
 
7.4%
e 395
 
6.9%
s 343
 
6.0%
u 298
 
5.2%
r 272
 
4.7%
n 270
 
4.7%
h 268
 
4.7%
T 235
 
4.1%
Other values (21) 1991
34.7%
Common
ValueCountFrequency (%)
582
50.1%
, 580
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 699
 
10.1%
582
 
8.4%
, 580
 
8.4%
t 549
 
8.0%
p 423
 
6.1%
e 395
 
5.7%
s 343
 
5.0%
u 298
 
4.3%
r 272
 
3.9%
n 270
 
3.9%
Other values (23) 2494
36.1%
Distinct18
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6119497
Minimum0
Maximum10
Zeros2
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:24.986475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11
median1
Q32
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4388425
Coefficient of variation (CV)0.89261005
Kurtosis9.5387819
Mean1.6119497
Median Absolute Deviation (MAD)0
Skewness2.7504392
Sum256.3
Variance2.0702677
MonotonicityNot monotonic
2023-04-10T15:30:25.085235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 86
54.1%
2 31
 
19.5%
3 9
 
5.7%
0.5 8
 
5.0%
6 5
 
3.1%
5 3
 
1.9%
0.3 2
 
1.3%
4 2
 
1.3%
0.05 2
 
1.3%
0.1 2
 
1.3%
Other values (8) 9
 
5.7%
ValueCountFrequency (%)
0 2
 
1.3%
0.05 2
 
1.3%
0.1 2
 
1.3%
0.2 1
 
0.6%
0.3 2
 
1.3%
0.5 8
 
5.0%
0.9 1
 
0.6%
1 86
54.1%
1.3 1
 
0.6%
1.5 1
 
0.6%
ValueCountFrequency (%)
10 1
 
0.6%
7 1
 
0.6%
6 5
 
3.1%
5 3
 
1.9%
4 2
 
1.3%
3 9
 
5.7%
2.5 1
 
0.6%
2 31
19.5%
1.5 1
 
0.6%
1.3 1
 
0.6%
Distinct21
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6898113
Minimum0
Maximum21
Zeros36
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:25.196271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.35
median1
Q32
95-th percentile5.1
Maximum21
Range21
Interquartile range (IQR)1.65

Descriptive statistics

Standard deviation2.4028105
Coefficient of variation (CV)1.4219401
Kurtosis29.771641
Mean1.6898113
Median Absolute Deviation (MAD)1
Skewness4.5205759
Sum268.68
Variance5.7734981
MonotonicityNot monotonic
2023-04-10T15:30:25.296704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 50
31.4%
0 36
22.6%
2 26
16.4%
3 12
 
7.5%
0.5 6
 
3.8%
4 6
 
3.8%
5 4
 
2.5%
6 3
 
1.9%
7 3
 
1.9%
1.5 2
 
1.3%
Other values (11) 11
 
6.9%
ValueCountFrequency (%)
0 36
22.6%
0.07 1
 
0.6%
0.1 1
 
0.6%
0.2 1
 
0.6%
0.3 1
 
0.6%
0.4 1
 
0.6%
0.45 1
 
0.6%
0.5 6
 
3.8%
1 50
31.4%
1.15 1
 
0.6%
ValueCountFrequency (%)
21 1
 
0.6%
14 1
 
0.6%
7 3
 
1.9%
6 3
 
1.9%
5 4
 
2.5%
4 6
 
3.8%
3 12
7.5%
2.43 1
 
0.6%
2 26
16.4%
1.58 1
 
0.6%

Time spend on Facebook? (In hours)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2836478
Minimum0
Maximum21
Zeros146
Zeros (%)91.8%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:25.393532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8214917
Coefficient of variation (CV)6.4216671
Kurtosis109.13316
Mean0.2836478
Median Absolute Deviation (MAD)0
Skewness9.9690541
Sum45.1
Variance3.3178322
MonotonicityNot monotonic
2023-04-10T15:30:25.482253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 146
91.8%
1 6
 
3.8%
2 2
 
1.3%
3 2
 
1.3%
21 1
 
0.6%
8 1
 
0.6%
0.1 1
 
0.6%
ValueCountFrequency (%)
0 146
91.8%
0.1 1
 
0.6%
1 6
 
3.8%
2 2
 
1.3%
3 2
 
1.3%
8 1
 
0.6%
21 1
 
0.6%
ValueCountFrequency (%)
21 1
 
0.6%
8 1
 
0.6%
3 2
 
1.3%
2 2
 
1.3%
1 6
 
3.8%
0.1 1
 
0.6%
0 146
91.8%
Distinct15
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7477987
Minimum0
Maximum16
Zeros12
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:25.586476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile5
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.784456
Coefficient of variation (CV)1.0209734
Kurtosis26.013508
Mean1.7477987
Median Absolute Deviation (MAD)0.9
Skewness3.9632472
Sum277.9
Variance3.1842831
MonotonicityNot monotonic
2023-04-10T15:30:25.690604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 63
39.6%
2 34
21.4%
3 15
 
9.4%
0 12
 
7.5%
0.5 11
 
6.9%
4 7
 
4.4%
5 5
 
3.1%
0.3 3
 
1.9%
0.1 2
 
1.3%
1.5 2
 
1.3%
Other values (5) 5
 
3.1%
ValueCountFrequency (%)
0 12
 
7.5%
0.1 2
 
1.3%
0.3 3
 
1.9%
0.5 11
 
6.9%
1 63
39.6%
1.5 2
 
1.3%
2 34
21.4%
2.3 1
 
0.6%
3 15
 
9.4%
4 7
 
4.4%
ValueCountFrequency (%)
16 1
 
0.6%
8 1
 
0.6%
7 1
 
0.6%
6 1
 
0.6%
5 5
 
3.1%
4 7
 
4.4%
3 15
9.4%
2.3 1
 
0.6%
2 34
21.4%
1.5 2
 
1.3%
Distinct18
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9608805
Minimum0
Maximum16
Zeros61
Zeros (%)38.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:25.818069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9392911
Coefficient of variation (CV)2.0182437
Kurtosis40.082689
Mean0.9608805
Median Absolute Deviation (MAD)1
Skewness5.7862895
Sum152.78
Variance3.7608499
MonotonicityNot monotonic
2023-04-10T15:30:25.931022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 61
38.4%
1 58
36.5%
2 9
 
5.7%
0.5 7
 
4.4%
3 7
 
4.4%
1.5 2
 
1.3%
0.3 2
 
1.3%
4 2
 
1.3%
0.1 2
 
1.3%
0.2 1
 
0.6%
Other values (8) 8
 
5.0%
ValueCountFrequency (%)
0 61
38.4%
0.01 1
 
0.6%
0.1 2
 
1.3%
0.15 1
 
0.6%
0.19 1
 
0.6%
0.2 1
 
0.6%
0.3 2
 
1.3%
0.33 1
 
0.6%
0.5 7
 
4.4%
0.6 1
 
0.6%
ValueCountFrequency (%)
16 1
 
0.6%
15 1
 
0.6%
8 1
 
0.6%
4 2
 
1.3%
3 7
 
4.4%
2 9
 
5.7%
1.5 2
 
1.3%
1 58
36.5%
0.6 1
 
0.6%
0.5 7
 
4.4%

Time spend on Reddit? (In hours)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
0
154 
1
 
3
4
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters159
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154
96.9%
1 3
 
1.9%
4 1
 
0.6%
2 1
 
0.6%

Length

2023-04-10T15:30:26.061295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T15:30:26.188576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 154
96.9%
1 3
 
1.9%
4 1
 
0.6%
2 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 154
96.9%
1 3
 
1.9%
4 1
 
0.6%
2 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 154
96.9%
1 3
 
1.9%
4 1
 
0.6%
2 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 154
96.9%
1 3
 
1.9%
4 1
 
0.6%
2 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 154
96.9%
1 3
 
1.9%
4 1
 
0.6%
2 1
 
0.6%

Time spend on Discord? (In hours)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20628931
Minimum0
Maximum9
Zeros141
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:26.285538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.88437102
Coefficient of variation (CV)4.2870424
Kurtosis64.905442
Mean0.20628931
Median Absolute Deviation (MAD)0
Skewness7.3049559
Sum32.8
Variance0.78211209
MonotonicityNot monotonic
2023-04-10T15:30:26.377362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 141
88.7%
1 11
 
6.9%
3 2
 
1.3%
4 1
 
0.6%
0.3 1
 
0.6%
0.5 1
 
0.6%
2 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
0 141
88.7%
0.3 1
 
0.6%
0.5 1
 
0.6%
1 11
 
6.9%
2 1
 
0.6%
3 2
 
1.3%
4 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
9 1
 
0.6%
4 1
 
0.6%
3 2
 
1.3%
2 1
 
0.6%
1 11
 
6.9%
0.5 1
 
0.6%
0.3 1
 
0.6%
0 141
88.7%

Time spend on Pinterest? (In hours)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17044025
Minimum0
Maximum4
Zeros133
Zeros (%)83.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:26.480306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.47568118
Coefficient of variation (CV)2.790897
Kurtosis27.22935
Mean0.17044025
Median Absolute Deviation (MAD)0
Skewness4.3521096
Sum27.1
Variance0.22627259
MonotonicityNot monotonic
2023-04-10T15:30:26.592719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 133
83.6%
1 20
 
12.6%
0.2 3
 
1.9%
2 1
 
0.6%
0.5 1
 
0.6%
4 1
 
0.6%
ValueCountFrequency (%)
0 133
83.6%
0.2 3
 
1.9%
0.5 1
 
0.6%
1 20
 
12.6%
2 1
 
0.6%
4 1
 
0.6%
ValueCountFrequency (%)
4 1
 
0.6%
2 1
 
0.6%
1 20
 
12.6%
0.5 1
 
0.6%
0.2 3
 
1.9%
0 133
83.6%

Time spend on Twitter? (In hours)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19981132
Minimum0
Maximum10
Zeros136
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:26.710780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8870014
Coefficient of variation (CV)4.4391949
Kurtosis95.843504
Mean0.19981132
Median Absolute Deviation (MAD)0
Skewness9.0056543
Sum31.77
Variance0.78677148
MonotonicityNot monotonic
2023-04-10T15:30:26.807912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 136
85.5%
1 13
 
8.2%
0.5 2
 
1.3%
2 2
 
1.3%
0.15 2
 
1.3%
0.2 1
 
0.6%
3 1
 
0.6%
10 1
 
0.6%
0.27 1
 
0.6%
ValueCountFrequency (%)
0 136
85.5%
0.15 2
 
1.3%
0.2 1
 
0.6%
0.27 1
 
0.6%
0.5 2
 
1.3%
1 13
 
8.2%
2 2
 
1.3%
3 1
 
0.6%
10 1
 
0.6%
ValueCountFrequency (%)
10 1
 
0.6%
3 1
 
0.6%
2 2
 
1.3%
1 13
 
8.2%
0.5 2
 
1.3%
0.27 1
 
0.6%
0.2 1
 
0.6%
0.15 2
 
1.3%
0 136
85.5%

Time spend on Telegram? (In hours)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34339623
Minimum0
Maximum15
Zeros131
Zeros (%)82.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:26.902659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5840793
Coefficient of variation (CV)4.6129783
Kurtosis64.649108
Mean0.34339623
Median Absolute Deviation (MAD)0
Skewness7.746268
Sum54.6
Variance2.5093074
MonotonicityNot monotonic
2023-04-10T15:30:27.101940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 131
82.4%
1 16
 
10.1%
0.5 2
 
1.3%
0.3 2
 
1.3%
0.7 1
 
0.6%
15 1
 
0.6%
3 1
 
0.6%
2 1
 
0.6%
0.1 1
 
0.6%
0.2 1
 
0.6%
Other values (2) 2
 
1.3%
ValueCountFrequency (%)
0 131
82.4%
0.1 1
 
0.6%
0.2 1
 
0.6%
0.3 2
 
1.3%
0.5 2
 
1.3%
0.7 1
 
0.6%
1 16
 
10.1%
2 1
 
0.6%
3 1
 
0.6%
4 1
 
0.6%
ValueCountFrequency (%)
15 1
 
0.6%
12 1
 
0.6%
4 1
 
0.6%
3 1
 
0.6%
2 1
 
0.6%
1 16
10.1%
0.7 1
 
0.6%
0.5 2
 
1.3%
0.3 2
 
1.3%
0.2 1
 
0.6%

Overall time spent on other application? (In hours)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1591195
Minimum0
Maximum23
Zeros34
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:27.219625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q33
95-th percentile7.1
Maximum23
Range23
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.9120921
Coefficient of variation (CV)1.3487406
Kurtosis17.838567
Mean2.1591195
Median Absolute Deviation (MAD)1
Skewness3.4555977
Sum343.3
Variance8.4802802
MonotonicityNot monotonic
2023-04-10T15:30:27.327981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 39
24.5%
0 34
21.4%
2 27
17.0%
3 12
 
7.5%
4 11
 
6.9%
5 8
 
5.0%
0.5 7
 
4.4%
0.3 4
 
2.5%
10 3
 
1.9%
12 2
 
1.3%
Other values (10) 12
 
7.5%
ValueCountFrequency (%)
0 34
21.4%
0.2 1
 
0.6%
0.3 4
 
2.5%
0.5 7
 
4.4%
1 39
24.5%
1.4 1
 
0.6%
1.5 2
 
1.3%
2 27
17.0%
2.5 1
 
0.6%
3 12
 
7.5%
ValueCountFrequency (%)
23 1
 
0.6%
12 2
 
1.3%
11 1
 
0.6%
10 3
 
1.9%
8 1
 
0.6%
7 1
 
0.6%
6 2
 
1.3%
5 8
5.0%
4 11
6.9%
3.5 1
 
0.6%
Distinct18
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7993711
Minimum0
Maximum16
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:27.437689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median8
Q314
95-th percentile16
Maximum16
Range16
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.0099963
Coefficient of variation (CV)0.56935845
Kurtosis-1.3023363
Mean8.7993711
Median Absolute Deviation (MAD)4
Skewness0.058631786
Sum1399.1
Variance25.100063
MonotonicityNot monotonic
2023-04-10T15:30:27.555694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
16 22
13.8%
10 16
10.1%
15 15
9.4%
4 14
8.8%
2 13
8.2%
12 12
7.5%
5 11
6.9%
7 10
 
6.3%
8 10
 
6.3%
6 9
 
5.7%
Other values (8) 27
17.0%
ValueCountFrequency (%)
0 3
 
1.9%
0.1 1
 
0.6%
1 3
 
1.9%
2 13
8.2%
3 7
4.4%
4 14
8.8%
5 11
6.9%
6 9
5.7%
7 10
6.3%
8 10
6.3%
ValueCountFrequency (%)
16 22
13.8%
15 15
9.4%
14 6
 
3.8%
13 1
 
0.6%
12 12
7.5%
11 1
 
0.6%
10 16
10.1%
9 5
 
3.1%
8 10
6.3%
7 10
6.3%
Distinct3
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
No
78 
Maybe
62 
Yes
19 

Length

Max length5
Median length3
Mean length3.2893082
Min length2

Characters and Unicode

Total characters523
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowYes
5th rowMaybe

Common Values

ValueCountFrequency (%)
No 78
49.1%
Maybe 62
39.0%
Yes 19
 
11.9%

Length

2023-04-10T15:30:27.681697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T15:30:27.804397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
no 78
49.1%
maybe 62
39.0%
yes 19
 
11.9%

Most occurring characters

ValueCountFrequency (%)
e 81
15.5%
N 78
14.9%
o 78
14.9%
M 62
11.9%
a 62
11.9%
y 62
11.9%
b 62
11.9%
Y 19
 
3.6%
s 19
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 364
69.6%
Uppercase Letter 159
30.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 81
22.3%
o 78
21.4%
a 62
17.0%
y 62
17.0%
b 62
17.0%
s 19
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
N 78
49.1%
M 62
39.0%
Y 19
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 523
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 81
15.5%
N 78
14.9%
o 78
14.9%
M 62
11.9%
a 62
11.9%
y 62
11.9%
b 62
11.9%
Y 19
 
3.6%
s 19
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 523
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 81
15.5%
N 78
14.9%
o 78
14.9%
M 62
11.9%
a 62
11.9%
y 62
11.9%
b 62
11.9%
Y 19
 
3.6%
s 19
 
3.6%
Distinct17
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6031447
Minimum0
Maximum15
Zeros4
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:27.907947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q11
median2
Q33
95-th percentile6.2
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2318018
Coefficient of variation (CV)0.85734837
Kurtosis7.9869943
Mean2.6031447
Median Absolute Deviation (MAD)1
Skewness2.4315058
Sum413.9
Variance4.9809394
MonotonicityNot monotonic
2023-04-10T15:30:28.017202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 51
32.1%
1 42
26.4%
3 20
 
12.6%
4 13
 
8.2%
5 8
 
5.0%
6 5
 
3.1%
0 4
 
2.5%
8 3
 
1.9%
10 3
 
1.9%
1.3 2
 
1.3%
Other values (7) 8
 
5.0%
ValueCountFrequency (%)
0 4
 
2.5%
0.1 1
 
0.6%
0.2 1
 
0.6%
0.5 2
 
1.3%
1 42
26.4%
1.3 2
 
1.3%
1.5 1
 
0.6%
2 51
32.1%
2.5 1
 
0.6%
3 20
 
12.6%
ValueCountFrequency (%)
15 1
 
0.6%
11 1
 
0.6%
10 3
 
1.9%
8 3
 
1.9%
6 5
 
3.1%
5 8
 
5.0%
4 13
 
8.2%
3 20
 
12.6%
2.5 1
 
0.6%
2 51
32.1%
Distinct15
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.290566
Minimum0
Maximum15
Zeros22
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-10T15:30:28.138886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6.4
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4653496
Coefficient of variation (CV)1.0763058
Kurtosis7.9537136
Mean2.290566
Median Absolute Deviation (MAD)1
Skewness2.51586
Sum364.2
Variance6.0779484
MonotonicityNot monotonic
2023-04-10T15:30:28.247749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 50
31.4%
2 28
17.6%
3 23
14.5%
0 22
13.8%
4 14
 
8.8%
5 6
 
3.8%
10 6
 
3.8%
0.5 3
 
1.9%
15 1
 
0.6%
13 1
 
0.6%
Other values (5) 5
 
3.1%
ValueCountFrequency (%)
0 22
13.8%
0.2 1
 
0.6%
0.5 3
 
1.9%
1 50
31.4%
1.5 1
 
0.6%
2 28
17.6%
2.5 1
 
0.6%
3 23
14.5%
3.5 1
 
0.6%
4 14
 
8.8%
ValueCountFrequency (%)
15 1
 
0.6%
13 1
 
0.6%
10 6
 
3.8%
6 1
 
0.6%
5 6
 
3.8%
4 14
8.8%
3.5 1
 
0.6%
3 23
14.5%
2.5 1
 
0.6%
2 28
17.6%
Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size287.0 B
True
89 
False
70 
ValueCountFrequency (%)
True 89
56.0%
False 70
44.0%
2023-04-10T15:30:28.384309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Interactions

2023-04-10T15:30:19.247812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:25.806165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:43.083415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:04.812689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:24.417463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:41.913356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:50.489439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:58.518840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:08.973264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:22.001010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:33.520704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:54.550562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:22.116651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:37.428608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:55.850645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:07.088725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:19.452614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:26.087315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:43.333357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:05.109495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:24.714268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:42.163296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:50.723786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:58.721909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:09.207585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:22.235330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:33.726938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:54.902671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:22.414334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:37.743824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:56.139924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:07.341811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:19.646451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:26.384118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:43.598919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:05.453169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:25.120431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:42.428854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:50.926836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:58.924991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:09.458484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:22.479195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:33.945668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:55.316261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:22.762315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:38.173203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:56.371305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:07.569021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:19.825702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:26.759066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:43.926967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:05.937396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:25.667163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:42.678796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:51.145567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:59.143695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:09.692117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:22.716615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:34.214104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:55.929423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:23.205776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:38.685597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:56.647360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:07.834506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:20.019727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:27.196429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:44.458094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:06.671630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:26.338888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:42.959985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:51.395509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:59.409252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:09.955746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:22.982659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:34.637611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:56.760624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:23.755707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:39.329227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:56.995559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:08.155480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:20.208456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:27.774418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:45.270404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:07.546422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:27.119953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:43.288037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:51.692281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:59.690432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:10.254224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:23.295696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:35.251390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:57.896041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:24.393016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:40.085411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:57.400667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:08.525234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:20.344744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:28.508619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:46.363902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:08.514951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:28.041582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:43.694199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:52.035979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:00.018490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:10.658279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:23.672860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:36.287850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:59.335621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:25.133105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:40.972836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:57.887900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:08.975366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:20.495293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:29.461523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:47.613607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:09.577196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:29.088243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:44.115972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:52.426484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:00.393397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:11.201293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:24.225530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:37.450195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:01.037953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:25.996746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:42.070192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:58.389514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:09.616527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:20.655606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:30.617499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:48.925788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:10.670660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:30.228569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:44.725200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:52.863905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:00.815167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:11.897853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:24.914442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:38.856443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:02.897513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:27.107946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:43.281121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:58.940648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:10.144729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:20.813247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:32.007800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:50.253608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:12.092232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:31.525174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:45.287564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:53.332555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:01.330677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:12.815894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:25.762210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:40.459588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:05.069115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:28.376989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:44.578995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:59.595490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:10.794471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:20.986080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:33.445024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:51.909468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:13.888656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:32.962334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:45.834315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:53.832404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:01.971148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:13.926385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:26.737206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:42.255643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:07.503377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:29.639411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:45.982601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:00.367608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:11.644351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:21.140549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:34.975945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:54.049563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:15.607033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:34.555710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:46.443547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:54.441671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:03.145760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:15.145211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:27.867392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:44.298149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:10.228721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:30.974693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:47.593472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:01.263498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:12.718671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:21.295013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:36.694286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:56.564598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:17.575290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:36.242782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:47.208986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:55.238352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:04.460426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:16.767331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:29.155143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:46.575475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:13.135156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:32.432475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:49.365399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:02.358925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:14.066907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:21.442475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:38.834408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:59.392091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:19.965382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:38.148584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:48.208754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:56.269360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:05.767662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:18.524915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:30.584717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:49.129762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:16.337350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:34.057974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:51.392080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:03.802437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:15.713627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:21.595199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:41.333831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:02.547596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:22.605359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:40.335608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:49.395945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:57.472208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:07.500549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:20.545326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:32.249605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:52.184751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:19.808921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:36.008546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:53.986420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:05.639919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:17.722518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:21.734073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:26:42.833445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:04.562754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:24.183114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:41.679030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:50.270769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:27:58.315757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:08.619897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:21.767577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:33.316844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:28:54.221058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:21.850636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:37.202243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:29:55.595348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:06.847254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T15:30:19.005598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-10T15:30:28.510478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Enter Marks(percentage) For 10th Grade:Enter Marks (percentage) For Previous Semester:How often do you use social media ? (In hours)Time spent on WhatsApp? (In hours)Time spend on Instagram? (In hours)Time spend on Facebook? (In hours)Time spend on YouTube? (In hours)Time spend on Snapchat? (In hours)Time spend on Discord? (In hours)Time spend on Pinterest? (In hours)Time spend on Twitter? (In hours)Time spend on Telegram? (In hours)Overall time spent on other application? (In hours)What is the maximum time that you have spent away from your phone? (In hours)Entertainment usage time while using phone(per day) (In hours)Productivity and finance time while using phone (per day)(In hours)Department:Choose Current year:According to you, what need does social media fulfill?How many social media platforms are you on?Time spend on Reddit? (In hours)Do you consider yourself to be addicted to social media?Impacted By Gadget
Enter Marks(percentage) For 10th Grade:1.0000.448-0.2510.2550.040-0.012-0.004-0.057-0.0380.0400.061-0.0770.021-0.2150.0130.1640.5100.4010.0000.0000.0000.0000.256
Enter Marks (percentage) For Previous Semester:0.4481.000-0.3270.135-0.0050.057-0.097-0.009-0.0250.1070.088-0.0520.001-0.0960.0930.2000.2270.2620.0000.0000.0000.1020.347
How often do you use social media ? (In hours)-0.251-0.3271.000-0.0350.094-0.0600.1040.0120.012-0.110-0.0740.016-0.0100.0890.027-0.0450.1070.0820.0000.0000.0000.0430.592
Time spent on WhatsApp? (In hours)0.2550.135-0.0351.0000.1990.2280.2850.2810.1500.1130.0490.0100.131-0.0880.1380.1670.0000.2290.0000.3950.1780.0820.000
Time spend on Instagram? (In hours)0.040-0.0050.0940.1991.0000.2550.1040.4390.2420.3050.1350.0570.040-0.1470.4770.2430.1920.1540.0000.3920.0330.1570.137
Time spend on Facebook? (In hours)-0.0120.057-0.0600.2280.2551.0000.1790.2470.2560.1870.3490.3820.219-0.2200.2170.0720.0000.0760.0000.6700.0000.0000.083
Time spend on YouTube? (In hours)-0.004-0.0970.1040.2850.1040.1791.0000.1810.2210.0160.1550.1200.2150.0090.2330.2440.0000.1600.0000.3710.3820.1740.000
Time spend on Snapchat? (In hours)-0.057-0.0090.0120.2810.4390.2470.1811.0000.1370.2400.2250.0860.1390.0230.2930.1550.1230.0420.0000.2520.4040.0660.000
Time spend on Discord? (In hours)-0.038-0.0250.0120.1500.2420.2560.2210.1371.0000.3940.4280.3150.2420.0350.1830.2410.1830.0870.2060.7740.5040.1160.000
Time spend on Pinterest? (In hours)0.0400.107-0.1100.1130.3050.1870.0160.2400.3941.0000.3560.2500.098-0.0350.1960.1300.0000.2120.0000.4240.6680.0990.000
Time spend on Twitter? (In hours)0.0610.088-0.0740.0490.1350.3490.1550.2250.4280.3561.0000.4050.186-0.0910.1630.1560.0000.1050.0580.6020.5660.1710.000
Time spend on Telegram? (In hours)-0.077-0.0520.0160.0100.0570.3820.1200.0860.3150.2500.4051.0000.245-0.0670.0820.0650.1140.0970.0000.4370.6070.0840.052
Overall time spent on other application? (In hours)0.0210.001-0.0100.1310.0400.2190.2150.1390.2420.0980.1860.2451.0000.0420.233-0.0100.0000.1110.0950.3830.5790.2420.069
What is the maximum time that you have spent away from your phone? (In hours)-0.215-0.0960.089-0.088-0.147-0.2200.0090.0230.035-0.035-0.091-0.0670.0421.000-0.1710.1230.0770.0000.0000.0820.0000.0920.000
Entertainment usage time while using phone(per day) (In hours)0.0130.0930.0270.1380.4770.2170.2330.2930.1830.1960.1630.0820.233-0.1711.0000.2870.0000.0000.0000.3560.1100.3520.054
Productivity and finance time while using phone\n(per day)(In hours)0.1640.200-0.0450.1670.2430.0720.2440.1550.2410.1300.1560.065-0.0100.1230.2871.0000.0000.1000.0000.2160.1500.0000.000
Department:0.5100.2270.1070.0000.1920.0000.0000.1230.1830.0000.0000.1140.0000.0770.0000.0001.0000.5100.0990.2520.0000.0000.249
Choose Current year:0.4010.2620.0820.2290.1540.0760.1600.0420.0870.2120.1050.0970.1110.0000.0000.1000.5101.0000.2100.0000.0700.1190.271
According to you, what need does social media fulfill?0.0000.0000.0000.0000.0000.0000.0000.0000.2060.0000.0580.0000.0950.0000.0000.0000.0990.2101.0000.1550.0000.0260.000
How many social media platforms are you on?0.0000.0000.0000.3950.3920.6700.3710.2520.7740.4240.6020.4370.3830.0820.3560.2160.2520.0000.1551.0000.7870.1860.111
Time spend on Reddit? (In hours)0.0000.0000.0000.1780.0330.0000.3820.4040.5040.6680.5660.6070.5790.0000.1100.1500.0000.0700.0000.7871.0000.1050.000
Do you consider yourself to be addicted to social media?0.0000.1020.0430.0820.1570.0000.1740.0660.1160.0990.1710.0840.2420.0920.3520.0000.0000.1190.0260.1860.1051.0000.000
Impacted By Gadget0.2560.3470.5920.0000.1370.0830.0000.0000.0000.0000.0000.0520.0690.0000.0540.0000.2490.2710.0000.1110.0000.0001.000

Missing values

2023-04-10T15:30:21.969847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-10T15:30:22.469678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Department:Choose Current year:Enter Marks(percentage) For 10th Grade:Enter Marks (percentage) For Previous Semester:How often do you use social media ? (In hours)According to you, what need does social media fulfill?How many social media platforms are you on?Time spent on WhatsApp? (In hours)Time spend on Instagram? (In hours)Time spend on Facebook? (In hours)Time spend on YouTube? (In hours)Time spend on Snapchat? (In hours)Time spend on Reddit? (In hours)Time spend on Discord? (In hours)Time spend on Pinterest? (In hours)Time spend on Twitter? (In hours)Time spend on Telegram? (In hours)Overall time spent on other application? (In hours)What is the maximum time that you have spent away from your phone? (In hours)Do you consider yourself to be addicted to social media?Entertainment usage time while using phone(per day) (In hours)Productivity and finance time while using phone (per day)(In hours)Impacted By Gadget
0Artificial IntelligenceThird year60.060.0010EntertainmentWhatsApp5.00.00.05.01.000.00.00.00.011.011.0No11.00.0No
1Artificial IntelligenceFirst year80.273.009Educational, Entertainment, Research/Reading, GamingWhatsApp, Instagram, YouTube, Snapchat1.02.00.03.01.000.00.00.00.04.014.0No4.04.0Yes
2Artificial IntelligenceFirst year80.080.149Educational, EntertainmentWhatsApp, YouTube1.00.00.03.00.000.00.00.00.02.012.0No2.03.0No
3Artificial IntelligenceFirst year84.479.869Educational, Entertainment, Research/ReadingWhatsApp, Instagram, YouTube, Snapchat, Pinterest, Twitter, Telegram0.05.00.00.50.500.00.00.00.00.01.0Yes6.03.0Yes
4Artificial IntelligenceFirst year82.074.008EntertainmentWhatsApp, Instagram, Facebook, YouTube, Snapchat, Pinterest, Telegram0.93.00.00.10.600.00.20.00.72.016.0Maybe2.02.0Yes
5Artificial IntelligenceFirst year78.071.008EntertainmentWhatsApp, Instagram, YouTube, Snapchat, Pinterest1.06.00.01.01.000.01.00.00.02.02.0Maybe5.00.0Yes
6Artificial IntelligenceFirst year82.676.008Educational, EntertainmentWhatsApp, Instagram, YouTube, Snapchat1.02.00.03.01.000.00.00.00.00.07.0Maybe2.01.0Yes
7Artificial IntelligenceFirst year78.076.008Entertainment, Research/ReadingWhatsApp, Instagram, YouTube, Snapchat, Pinterest, Twitter0.55.00.00.51.000.00.20.50.01.08.0No5.01.0Yes
8Artificial IntelligenceFirst year82.460.007Educational, Entertainment, Research/Reading, GamingWhatsApp, Instagram, Facebook, YouTube, Snapchat1.02.00.01.01.000.00.00.00.02.010.0Maybe2.00.0Yes
9Artificial IntelligenceFirst year78.682.297Educational, Entertainment, Research/Reading, GamingWhatsApp, Instagram, Facebook, YouTube, Discord, Twitter, Telegram1.00.50.01.00.000.00.00.00.02.015.0No1.01.0No
Department:Choose Current year:Enter Marks(percentage) For 10th Grade:Enter Marks (percentage) For Previous Semester:How often do you use social media ? (In hours)According to you, what need does social media fulfill?How many social media platforms are you on?Time spent on WhatsApp? (In hours)Time spend on Instagram? (In hours)Time spend on Facebook? (In hours)Time spend on YouTube? (In hours)Time spend on Snapchat? (In hours)Time spend on Reddit? (In hours)Time spend on Discord? (In hours)Time spend on Pinterest? (In hours)Time spend on Twitter? (In hours)Time spend on Telegram? (In hours)Overall time spent on other application? (In hours)What is the maximum time that you have spent away from your phone? (In hours)Do you consider yourself to be addicted to social media?Entertainment usage time while using phone(per day) (In hours)Productivity and finance time while using phone (per day)(In hours)Impacted By Gadget
149Information TechnologySecond year84.075.137Educational, Entertainment, Research/ReadingWhatsApp, Instagram, YouTube, Snapchat, No2.02.00.03.01.000.00.00.00.08.015.0No4.02.0Yes
150Information TechnologyFirst year85.065.0010Educational, Entertainment, Research/Reading, GamingWhatsApp, Instagram, Facebook, YouTube, Snapchat, Discord, Twitter1.02.00.03.02.000.50.00.00.01.016.0Maybe4.03.0Yes
151Information TechnologySecond year85.063.0010Educational, Entertainment, Research/ReadingWhatsApp, Instagram, Facebook, YouTube, Snapchat, Discord, Twitter,3.07.00.05.01.002.00.01.01.02.010.0Yes5.02.0Yes
152Information TechnologySecond year62.069.007EntertainmentWhatsApp, Instagram, YouTube, Snapchat, Discord, Pinterest1.03.00.02.01.000.00.00.00.02.09.0No3.05.0No
153Information TechnologySecond year70.075.006Educational, Entertainment, GamingWhatsApp, Instagram, Facebook, YouTube, Snapchat, Discord, Pinterest, Telegram1.03.00.01.01.009.01.00.00.00.07.0Maybe2.00.0No
154Information TechnologySecond year84.854.876Educational, Entertainment, Research/ReadingWhatsApp, Instagram, YouTube, Snapchat, Twitter1.01.00.01.01.000.00.01.00.00.06.0No3.03.0Yes
155Information TechnologySecond year75.077.505Educational, Entertainment, GamingWhatsApp, YouTube1.00.00.03.00.001.00.00.012.04.010.0No2.01.0No
156MechanicalThird year52.057.007EntertainmentWhatsApp, Instagram, YouTube, Snapchat1.07.00.06.04.000.01.00.00.00.08.0No2.02.0No
157MechanicalThird year80.089.006EducationalWhatsApp, Instagram, Facebook, YouTube, Snapchat, Discord, Pinterest, Twitter, Telegram1.01.01.01.01.001.01.01.01.01.02.0No1.01.0No
158MechanicalThird year74.074.004EducationalWhatsApp, Instagram, Snapchat, Twitter, Telegram1.01.01.01.01.000.00.00.00.00.02.0No2.02.0No